 Inpainting methods interpolate missing or
occluded regions of an input by filling these regions with plausible pixels.
Most algorithms rely on neighboring pixels to propagate the pixel information to target
regions [1]}, [2]}.
These methods generally handle images with narrow holes and do not perform well on
large holes [3]}, [4]}.
Liu  [5]} recently proposed to solve arbitrary holes by
training convolutional neural networks (CNN). To solve an inpainting task, they use
partial convolution with binary a mask as prior for the missing holes.
Iizuka  [6]} propose local and global discriminator networks to maintain consistent results for missing regions.
However, these methods applied to known boundary images and are not suitable for handling images
beyond the field of view.
